paddlepaddle--paddle/test/legacy_test/test_save_inference_model_conditional_op.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import tempfile
import unittest

import paddle
import paddle.nn.functional as F


def getModelOp(model_path):
    model_bytes = paddle.static.load_from_file(model_path)
    pg = paddle.static.deserialize_program(model_bytes)
    main_block = pg.desc.block(0)
    size = main_block.op_size()

    result = set()
    for i in range(0, size):
        result.add(main_block.op(i).type())

    return result


def GetPirModelOp(model_path):
    recover_program = paddle.static.Program()
    # pir_version
    paddle.base.core.deserialize_pir_program(
        model_path,
        recover_program,
        1,
    )

    return recover_program


class WhileNet(paddle.nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        y = paddle.rand(shape=[1, 3, 4, 4])

        w1 = paddle.shape(y)[2]
        w2 = paddle.assign(paddle.shape(x)[2])

        while w2 != w1:
            x = F.avg_pool2d(x, kernel_size=3, padding=1, stride=2)
            w2 = paddle.shape(x)[2]

        return x + y


class ForNet(paddle.nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        y = paddle.randint(low=0, high=5, shape=[1], dtype='int32')
        z = paddle.randint(low=0, high=5, shape=[1], dtype='int32')
        for i in range(0, z):
            x = x + i

        return x + y


class IfElseNet(paddle.nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        y = paddle.to_tensor([5], dtype='int32')
        if x > y:
            x = x + 1
        else:
            x = x - 1
        return x


class TestConditionalOp(unittest.TestCase):
    def test_while_op(self):
        paddle.disable_static()
        net = WhileNet()
        net = paddle.jit.to_static(
            net,
            input_spec=[
                paddle.static.InputSpec(shape=[1, 3, 8, 8], dtype='float32')
            ],
            full_graph=True,
        )
        root_path = tempfile.TemporaryDirectory()
        model_file = os.path.join(root_path.name, "while_net")
        paddle.jit.save(net, model_file)

        paddle.enable_static()
        if paddle.framework.use_pir_api():
            program = GetPirModelOp(model_file + ".json")
            self.assertEqual(program.global_block().ops[-2].name(), "pd_op.add")
            self.assertEqual(
                program.global_block().ops[-3].result(1).shape, [1, 3, -1, -1]
            )
            self.assertEqual(
                program.global_block().ops[-3].name(), "pd_op.while"
            )
        else:
            right_pdmodel = {
                "uniform_random",
                "shape",
                "slice",
                "not_equal",
                "while",
                "elementwise_add",
            }
            pdmodel = getModelOp(model_file + ".pdmodel")
            self.assertTrue(
                len(right_pdmodel.difference(pdmodel)) == 0,
                "The while op is pruned by mistake.",
            )
        root_path.cleanup()

    def test_for_op(self):
        paddle.disable_static()
        net = ForNet()
        net = paddle.jit.to_static(
            net,
            input_spec=[paddle.static.InputSpec(shape=[1], dtype='int32')],
            full_graph=True,
        )
        root_path = tempfile.TemporaryDirectory()
        model_file = os.path.join(root_path.name, "for_net")
        paddle.jit.save(net, model_file)

        paddle.enable_static()
        if paddle.framework.use_pir_api():
            program = GetPirModelOp(model_file + ".json")
            self.assertEqual(program.global_block().ops[-2].name(), "pd_op.add")
            self.assertEqual(
                program.global_block().ops[-3].name(), "pd_op.while"
            )
        else:
            right_pdmodel = {
                "randint",
                "fill_constant",
                "cast",
                "less_than",
                "while",
                "elementwise_add",
            }

            pdmodel = getModelOp(model_file + ".pdmodel")
            self.assertTrue(
                len(right_pdmodel.difference(pdmodel)) == 0,
                "The for op is pruned by mistake.",
            )
        root_path.cleanup()

    def test_if_op(self):
        paddle.disable_static()
        net = IfElseNet()
        net = paddle.jit.to_static(
            net,
            input_spec=[paddle.static.InputSpec(shape=[1], dtype='int32')],
            full_graph=True,
        )
        root_path = tempfile.TemporaryDirectory()
        model_file = os.path.join(root_path.name, "if_net")
        paddle.jit.save(net, model_file)

        paddle.enable_static()
        if paddle.framework.use_pir_api():
            program = GetPirModelOp(model_file + ".json")
            op_list = [
                "pd_op.data",
                "pd_op.full",
                "pd_op.assign_value_",
                "pd_op.cast",
                "pd_op.greater_than",
                "pd_op.if",
                "pd_op.fetch",
            ]
            i = 0
            for op in program.global_block().ops:
                self.assertEqual(op.name(), op_list[i])
                i = i + 1
        else:
            right_pdmodel = {
                "assign_value",
                "greater_than",
                "cast",
                "conditional_block",
                "logical_not",
                "select_input",
            }

            pdmodel = getModelOp(model_file + ".pdmodel")
            self.assertTrue(
                len(right_pdmodel.difference(pdmodel)) == 0,
                "The if op is pruned by mistake.",
            )
        root_path.cleanup()


if __name__ == '__main__':
    unittest.main()